1. Gastos (cálculos antiguos)

Gastos_casa %>% 
  dplyr::select(-Tiempo,-link) %>%
  dplyr::select(fecha, gasto, monto, gastador,obs) %>% tail(30) %>% 
  knitr::kable(format = "markdown", size=12)
fecha gasto monto gastador obs
25/7/2022 Comida 61470 Tami NA
25/7/2022 Comida 16100 Tami NA
25/7/2022 Cortina baño 29120 Tami NA
28/7/2022 Electricidad 78798 Andrés NA
29/7/2022 Netflix 8320 Tami NA
30/7/2022 Comida 36170 Tami NA
31/7/2022 Parafina 22060 Tami NA
1/8/2022 Comida 11670 Andrés NA
8/8/2022 Comida 17890 Tami NA
8/8/2022 Comida 41390 Tami NA
19/8/2022 VTR 21990 Andrés NA
18/8/2022 Comida 21860 Andrés NA
19/8/2022 Comida 5213 Andrés NA
22/8/2022 Parafina 23300 Tami NA
24/8/2022 Comida 57780 Tami NA
26/8/2022 mantencion toyotomi 34000 Andrés mantencion toyotomi
27/8/2022 Comida 19410 Tami NA
29/8/2022 Netflix 8320 Tami NA
31/8/2022 Incoludido 21000 Tami NA
31/8/2022 Electricidad 89272 Andrés PAC ENEL 01686518
31/8/2022 Enceres 12000 Andrés Visita gasfiter
3/9/2022 Comida 59225 Andrés Lider
3/9/2022 Comida 21350 Andrés Laflordeloto.cl
5/9/2022 Comida 78035 Tami NA
7/9/2022 Enceres 15400 Andrés era del mes pasado pero igual
10/9/2022 Agua 10860 Andrés PAC AGUAS ANDIN 000000005687837
10/9/2022 basureros 20000 Andrés basureros
10/9/2022 Comida 41870 Andrés NA
31/3/2019 Comida 9000 Andrés NA
8/9/2019 Comida 24588 Andrés Super Lider

#para ver las diferencias depués de la diosi
Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
    dplyr::group_by(gastador, fecha,.drop = F) %>% 
    dplyr::summarise(gasto_media=mean(monto,na.rm=T)) %>% 
    dplyr::mutate(treat=ifelse(fecha>"2019-W26",1,0)) %>%
    #dplyr::mutate(fecha_simp=lubridate::week(fecha)) %>%#después de  diosi. Junio 24, 2019 
    dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>% 
    assign("ts_gastos_casa_week_treat", ., envir = .GlobalEnv) 

gplots::plotmeans(gasto_media ~ gastador_nombre, main="Promedio de gasto por gastador", data=ts_gastos_casa_week_treat,ylim=c(0,75000), xlab="", ylab="")

par(mfrow=c(1,2)) 
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Antes de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==0,], xlab="", ylab="", ylim=c(0,70000))

gplots::plotmeans(gasto_media ~ gastador_nombre, main="Después de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==1,], xlab="", ylab="",ylim=c(0,70000))

library(ggiraph)
library(scales)
#if( requireNamespace("dplyr", quietly = TRUE)){
gg <- Gastos_casa %>%
  dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
  dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
  dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%
  dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
  dplyr::mutate(treat=ifelse(fecha_week>"2019 W26",1,0)) %>%
  dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>% 
#  dplyr::mutate(week=as.Date(as.character(lubridate::floor_date(fecha, "week"))))%>%
  #dplyr::mutate(fecha_week= lubridate::parse_date_time(fecha_week, c("%Y-W%V"),exact=T)) %>% 
  dplyr::group_by(gastador_nombre, fecha_simp) %>%
  dplyr::summarise(monto_total=sum(monto)) %>%
  dplyr::mutate(tooltip= paste0(substr(gastador_nombre,1,1),"=",round(monto_total/1000,2))) %>%
  ggplot(aes(hover_css = "fill:none;")) +#, ) +
  #stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
  geom_line(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre)),size=1,alpha=.5) +
                       ggiraph::geom_point_interactive(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre),tooltip=tooltip),size = 1) +
  #geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
 # guides(color = F)+
  sjPlot::theme_sjplot2() +
  geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
  labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") + ggtitle( "Figura 4. Gastos por Gastador") +
  scale_y_continuous(labels = f <- function(x) paste0(x/1000)) + 
  scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
  scale_x_yearweek(date_breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
  theme(axis.text.x = element_text(vjust = 0.5,angle = 35), legend.position='bottom')+
     theme(
    panel.border = element_blank(), 
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), 
    axis.line = element_line(colour = "black")
    )

#  x <- girafe(ggobj = gg)
#  x <- girafe_options(x = x,
#                      opts_hover(css = "stroke:red;fill:orange") )
#  if( interactive() ) print(x)

#}
tooltip_css <- "background-color:gray;color:white;font-style:italic;padding:10px;border-radius:10px 20px 10px 20px;"

#ggiraph(code = {print(gg)}, tooltip_extra_css = tooltip_css, tooltip_opacity = .75 )

x <- girafe(ggobj = gg)
x <- girafe_options(x,
  opts_zoom(min = 1, max = 3), opts_hover(css =tooltip_css))
x
plot<-Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(month=as.Date(as.character(lubridate::floor_date(fecha, "month"))))%>%
    dplyr::group_by(month)%>%
    dplyr::summarise(gasto_total=sum(monto)/1000) %>%
      ggplot2::ggplot(aes(x = month, y = gasto_total)) +
      geom_point()+
      geom_line(size=1) +
      sjPlot::theme_sjplot2() +
      geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
      geom_vline(xintercept = as.Date("2019-03-23"),linetype = "dashed", color="red") +
      labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") + 
      ggtitle( "Figura. Suma de Gastos por Mes") +        
      scale_x_date(breaks = "1 month", minor_breaks = "1 month", labels=scales::date_format("%m/%y")) +
      theme(axis.text.x = element_text(vjust = 0.5,angle = 45)) 
plotly::ggplotly(plot)  
plot2<-Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::group_by(day)%>%
    summarise(gasto_total=sum(monto)/1000) %>%
      ggplot2::ggplot(aes(x = day, y = gasto_total)) +
      geom_line(size=1) +
      sjPlot::theme_sjplot2() +
      geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
      geom_vline(xintercept = as.Date("2020-03-23"),linetype = "dashed", color="red") +
      labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") + 
      ggtitle( "Figura. Suma de Gastos por Día") +        
      scale_x_date(breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
      theme(axis.text.x = element_text(vjust = 0.5,angle = 45)) 
plotly::ggplotly(plot2)  
tsData <- Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::group_by(day)%>%
    summarise(gasto_total=sum(monto))%>%
    dplyr::mutate(covid=case_when(day>as.Date("2019-06-02")~1,TRUE~0))%>%
    dplyr::mutate(covid=case_when(day>as.Date("2020-03-10")~covid+1,TRUE~covid))%>%
    dplyr::mutate(covid=as.factor(covid))%>%
  data.frame()
tsData_gastos <-ts(tsData$gasto_total, frequency=7)
mstsData_gastos <- forecast::msts(Gastos_casa$monto, seasonal.periods=c(7,30))
  tsData_gastos = decompose(tsData_gastos)
#plot(tsData_Santiago, title="Descomposición del número de casos confirmados para Santiago")
forecast::autoplot(tsData_gastos, main="Descomposición de los Gastos Diarios")+
    theme_bw()+ labs(x="Weeks")

tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
#tsData_gastos$trend
#Using the inputted variables, a Type-2 Sum Squares ANCOVA Lagged Dependent Variable model is fitted which estimates the difference in means between interrupted and non-interrupted time periods, while accounting for the lag of the dependent variable and any further specified covariates.
#Typically such analyses use Auto-regressive Integrated Moving Average (ARIMA) models to handle the serial dependence of the residuals of a linear model, which is estimated either as part of the ARIMA process or through a standard linear regression modeling process [9,17]. All such time series methods enable the effect of the event to be separated from general trends and serial dependencies in time, thereby enabling valid statistical inferences to be made about whether an intervention has had an effect on a time series.
   #it uses Type-2 Sum Squares ANCOVA Lagged Dependent Variable model
   #ITSA model da cuenta de observaciones autocorrelacionadas e impactos dinámicos mediante una regresión de deltas en rezagados. Una vez que se incorporan en el modelo, se controlan. 
#residual autocorrelation assumptions
#TSA allows the model to account for baseline levels and trends present in the data therefore allowing us to attribute significant changes to the interruption
#RDestimate(all~agecell,data=metro_region,cutpoint = 21)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()

itsa_metro_region_quar2<-
        its.analysis::itsa.model(time = "day", depvar = "trend",data=tsdata_gastos_trend,
                                 interrupt_var = "covid", 
                                 alpha = 0.05,no.plots = F, bootstrap = TRUE, Reps = 10000, print = F) 

print(itsa_metro_region_quar2)
## [[1]]
## [1] "ITSA Model Fit"
## 
## $aov.result
## Anova Table (Type II tests)
## 
## Response: depvar
##                   Sum Sq  Df   F value Pr(>F)    
## interrupt_var 4.3273e+08   2    4.8228 0.0084 ** 
## lag_depvar    7.6074e+10   1 1695.7072 <2e-16 ***
## Residuals     2.1803e+10 486                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## $tukey.result
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: stats::aov(formula = x$depvar ~ x$interrupt_var)
## 
## $`x$interrupt_var`
##          diff        lwr      upr    p adj
## 1-0  7228.838   895.5205 13562.16 0.020562
## 2-0 27533.555 21687.1722 33379.94 0.000000
## 2-1 20304.717 16757.6044 23851.83 0.000000
## 
## 
## $data
##        depvar interrupt_var lag_depvar
## 2    19269.29             0   16010.00
## 3    24139.00             0   19269.29
## 4    23816.14             0   24139.00
## 5    26510.14             0   23816.14
## 6    23456.71             0   26510.14
## 7    24276.71             0   23456.71
## 8    18818.71             0   24276.71
## 9    18517.14             0   18818.71
## 10   15475.29             0   18517.14
## 11   16365.29             0   15475.29
## 12   12621.29             0   16365.29
## 13   12679.86             0   12621.29
## 14   13440.71             0   12679.86
## 15   15382.86             0   13440.71
## 16   13459.71             0   15382.86
## 17   14644.14             0   13459.71
## 18   13927.00             0   14644.14
## 19   22034.57             0   13927.00
## 20   20986.00             0   22034.57
## 21   20390.57             0   20986.00
## 22   22554.14             0   20390.57
## 23   21782.57             0   22554.14
## 24   22529.57             0   21782.57
## 25   24642.71             0   22529.57
## 26   17692.29             0   24642.71
## 27   19668.29             0   17692.29
## 28   28640.00             0   19668.29
## 29   28706.00             0   28640.00
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## 232  74875.14             2   80355.00
## 233  81347.00             2   74875.14
## 234  66062.43             2   81347.00
## 235  56946.43             2   66062.43
## 236  47732.14             2   56946.43
## 237  38129.71             2   47732.14
## 238  42928.29             2   38129.71
## 239  45392.57             2   42928.29
## 240  37895.43             2   45392.57
## 241  30660.29             2   37895.43
## 242  42430.86             2   30660.29
## 243  35845.14             2   42430.86
## 244  40350.43             2   35845.14
## 245  31494.71             2   40350.43
## 246  30013.29             2   31494.71
## 247  34197.57             2   30013.29
## 248  37430.14             2   34197.57
## 249  26932.43             2   37430.14
## 250  33729.86             2   26932.43
## 251  38081.43             2   33729.86
## 252  44028.00             2   38081.43
## 253  47139.71             2   44028.00
## 254  46558.86             2   47139.71
## 255  58350.57             2   46558.86
## 256  78380.00             2   58350.57
## 257  78168.29             2   78380.00
## 258  70510.86             2   78168.29
## 259  72207.14             2   70510.86
## 260  67881.00             2   72207.14
## 261  69536.43             2   67881.00
## 262  62390.71             2   69536.43
## 263  50113.14             2   62390.71
## 264  45565.57             2   50113.14
## 265  45805.29             2   45565.57
## 266  41348.57             2   45805.29
## 267  51426.86             2   41348.57
## 268  47160.57             2   51426.86
## 269  51907.43             2   47160.57
## 270  49751.43             2   51907.43
## 271  54407.43             2   49751.43
## 272  54746.29             2   54407.43
## 273  61634.57             2   54746.29
## 274  58926.43             2   61634.57
## 275  69999.29             2   58926.43
## 276  63044.86             2   69999.29
## 277  63285.29             2   63044.86
## 278  61395.43             2   63285.29
## 279  67969.43             2   61395.43
## 280  60792.57             2   67969.43
## 281  56859.14             2   60792.57
## 282  44899.43             2   56859.14
## 283  43064.14             2   44899.43
## 284  62790.29             2   43064.14
## 285  69120.71             2   62790.29
## 286  69589.43             2   69120.71
## 287  66633.29             2   69589.43
## 288  65588.57             2   66633.29
## 289  70168.57             2   65588.57
## 290  74644.71             2   70168.57
## 291  52891.00             2   74644.71
## 292  41560.57             2   52891.00
## 293  34704.86             2   41560.57
## 294  46520.00             2   34704.86
## 295  50231.00             2   46520.00
## 296  49216.71             2   50231.00
## 297  76914.86             2   49216.71
## 298  83720.71             2   76914.86
## 299  84485.00             2   83720.71
## 300  89765.00             2   84485.00
## 301  87702.86             2   89765.00
## 302  82013.86             2   87702.86
## 303  85982.43             2   82013.86
## 304  57248.43             2   85982.43
## 305  52968.43             2   57248.43
## 306  52601.86             2   52968.43
## 307  45493.29             2   52601.86
## 308  42298.86             2   45493.29
## 309  46423.71             2   42298.86
## 310  37898.00             2   46423.71
## 311  36435.14             2   37898.00
## 312  30209.57             2   36435.14
## 313  34541.86             2   30209.57
## 314  33604.71             2   34541.86
## 315  37990.71             2   33604.71
## 316  35683.43             2   37990.71
## 317  65201.86             2   35683.43
## 318  62730.57             2   65201.86
## 319  64589.14             2   62730.57
## 320  73744.86             2   64589.14
## 321  76477.71             2   73744.86
## 322 105647.43             2   76477.71
## 323 103790.29             2  105647.43
## 324  76122.29             2  103790.29
## 325  74746.14             2   76122.29
## 326  72865.71             2   74746.14
## 327  63652.57             2   72865.71
## 328  60358.29             2   63652.57
## 329  25957.14             2   60358.29
## 330  30178.43             2   25957.14
## 331  30681.57             2   30178.43
## 332  33337.29             2   30681.57
## 333  32582.71             2   33337.29
## 334  39184.43             2   32582.71
## 335  40415.71             2   39184.43
## 336  34975.43             2   40415.71
## 337  34076.14             2   34975.43
## 338  34221.14             2   34076.14
## 339  28862.57             2   34221.14
## 340  35729.86             2   28862.57
## 341  36489.29             2   35729.86
## 342  36785.14             2   36489.29
## 343  37787.71             2   36785.14
## 344  39832.14             2   37787.71
## 345  41917.86             2   39832.14
## 346  41633.57             2   41917.86
## 347  33557.00             2   41633.57
## 348  22759.57             2   33557.00
## 349  28877.86             2   22759.57
## 350  27574.00             2   28877.86
## 351  27104.71             2   27574.00
## 352  24376.14             2   27104.71
## 353  29732.29             2   24376.14
## 354  34030.00             2   29732.29
## 355  39139.71             2   34030.00
## 356  37066.57             2   39139.71
## 357  38509.29             2   37066.57
## 358  40957.29             2   38509.29
## 359  49423.00             2   40957.29
## 360  50053.29             2   49423.00
## 361  50284.14             2   50053.29
## 362  53103.86             2   50284.14
## 363  50223.00             2   53103.86
## 364  49587.14             2   50223.00
## 365  41167.71             2   49587.14
## 366  37958.71             2   41167.71
## 367  33582.29             2   37958.71
## 368  31039.43             2   33582.29
## 369  26526.57             2   31039.43
## 370  34869.43             2   26526.57
## 371  37487.43             2   34869.43
## 372  46514.43             2   37487.43
## 373  39613.43             2   46514.43
## 374  38980.57             2   39613.43
## 375  37306.14             2   38980.57
## 376  36771.29             2   37306.14
## 377  26317.00             2   36771.29
## 378  31580.71             2   26317.00
## 379  23626.57             2   31580.71
## 380  33035.71             2   23626.57
## 381  44864.57             2   33035.71
## 382  48946.14             2   44864.57
## 383  46969.57             2   48946.14
## 384  49249.57             2   46969.57
## 385  56370.14             2   49249.57
## 386  67228.71             2   56370.14
## 387  59457.29             2   67228.71
## 388  53124.71             2   59457.29
## 389  52814.14             2   53124.71
## 390  61262.00             2   52814.14
## 391  61861.14             2   61262.00
## 392  71784.71             2   61861.14
## 393  59313.29             2   71784.71
## 394  61107.00             2   59313.29
## 395  60603.43             2   61107.00
## 396  60012.57             2   60603.43
## 397  58280.43             2   60012.57
## 398  56862.71             2   58280.43
## 399  41704.43             2   56862.71
## 400  51533.00             2   41704.43
## 401  50388.71             2   51533.00
## 402  49205.29             2   50388.71
## 403  56533.29             2   49205.29
## 404  47996.14             2   56533.29
## 405  47207.57             2   47996.14
## 406  45292.00             2   47207.57
## 407  40343.43             2   45292.00
## 408  39004.86             2   40343.43
## 409  36788.43             2   39004.86
## 410  30027.57             2   36788.43
## 411  39040.14             2   30027.57
## 412  42390.14             2   39040.14
## 413  36291.14             2   42390.14
## 414  30668.29             2   36291.14
## 415  47693.00             2   30668.29
## 416  52094.43             2   47693.00
## 417  56592.57             2   52094.43
## 418  47971.43             2   56592.57
## 419  43762.43             2   47971.43
## 420  42246.71             2   43762.43
## 421  46352.43             2   42246.71
## 422  33094.86             2   46352.43
## 423  32784.86             2   33094.86
## 424  26212.43             2   32784.86
## 425  32611.57             2   26212.43
## 426  42144.86             2   32611.57
## 427  50034.86             2   42144.86
## 428  46332.00             2   50034.86
## 429  42976.29             2   46332.00
## 430  39456.29             2   42976.29
## 431  39328.29             2   39456.29
## 432  35296.14             2   39328.29
## 433  30875.43             2   35296.14
## 434  27709.00             2   30875.43
## 435  29513.29             2   27709.00
## 436  31630.43             2   29513.29
## 437  29346.14             2   31630.43
## 438  34916.86             2   29346.14
## 439  42020.86             2   34916.86
## 440  38303.00             2   42020.86
## 441  37966.43             2   38303.00
## 442  41408.14             2   37966.43
## 443  38988.14             2   41408.14
## 444  43555.29             2   38988.14
## 445  38114.00             2   43555.29
## 446  27847.86             2   38114.00
## 447  26517.00             2   27847.86
## 448  39518.29             2   26517.00
## 449  39153.71             2   39518.29
## 450  45623.14             2   39153.71
## 451  40627.43             2   45623.14
## 452  41027.71             2   40627.43
## 453  42882.86             2   41027.71
## 454  47139.43             2   42882.86
## 455  35547.57             2   47139.43
## 456  41099.00             2   35547.57
## 457  35859.57             2   41099.00
## 458  44524.57             2   35859.57
## 459  48554.29             2   44524.57
## 460  51554.29             2   48554.29
## 461  47810.29             2   51554.29
## 462  50490.00             2   47810.29
## 463  50720.71             2   50490.00
## 464  52720.71             2   50720.71
## 465  52145.57             2   52720.71
## 466  55515.57             2   52145.57
## 467  52457.00             2   55515.57
## 468  58239.57             2   52457.00
## 469  50523.57             2   58239.57
## 470  47788.57             2   50523.57
## 471  46170.00             2   47788.57
## 472  42305.57             2   46170.00
## 473  46605.57             2   42305.57
## 474  55149.57             2   46605.57
## 475  48769.57             2   55149.57
## 476  50719.43             2   48769.57
## 477  44753.71             2   50719.43
## 478  42898.00             2   44753.71
## 479  46141.14             2   42898.00
## 480  34022.57             2   46141.14
## 481  26651.86             2   34022.57
## 482  28791.86             2   26651.86
## 483  31879.00             2   28791.86
## 484  33584.71             2   31879.00
## 485  34690.43             2   33584.71
## 486  27410.43             2   34690.43
## 487  41755.00             2   27410.43
## 488  49379.57             2   41755.00
## 489  57198.86             2   49379.57
## 490  51144.57             2   57198.86
## 491  56677.43             2   51144.57
## 
## $alpha
## [1] 0.05
## 
## $itsa.result
## [1] "Significant variation between time periods with chosen alpha"
## 
## $group.means
##   interrupt_var count     mean      s.d.
## 1             0    37 22066.04  6308.636
## 2             1   120 29463.10  9187.258
## 3             2   334 49767.82 16111.292
## 
## $dependent
##   [1]  19269.29  24139.00  23816.14  26510.14  23456.71  24276.71  18818.71
##   [8]  18517.14  15475.29  16365.29  12621.29  12679.86  13440.71  15382.86
##  [15]  13459.71  14644.14  13927.00  22034.57  20986.00  20390.57  22554.14
##  [22]  21782.57  22529.57  24642.71  17692.29  19668.29  28640.00  28706.00
##  [29]  28331.57  25617.86  27223.29  31622.57  32021.43  33634.57  30784.86
##  [36]  34770.57  38443.00  35073.00  31422.29  30103.29  19319.29  27926.29
##  [43]  30715.43  31962.29  39790.14  39211.57  44548.57  49398.00  41039.00
##  [50]  34821.29  29123.57  21275.71  28476.14  24561.86  20323.57  25370.00
##  [57]  26811.86  27151.86  27623.29  22896.57  41889.29  44000.14  38558.00
##  [64]  43373.86  49001.00  61213.29  58939.57  42046.86  39191.71  42646.43
##  [71]  36121.57  30915.57  20273.43  23938.29  19274.29  21662.29  15819.00
##  [78]  18126.14  17240.71  16127.71  13917.14  15379.86  19510.14  24567.29
##  [85]  25700.43  25729.00  26435.00  31157.14  29818.43  30962.43  28746.71
##  [92]  27830.71  28252.14  28717.57  21365.43  24816.86  16838.57  15529.14
##  [99]  13286.29  13629.43  14404.86  19524.86  18475.71  22495.00  22254.57
## [106]  24173.29  27466.43  24602.43  20531.14  20846.43  23875.71  36312.71
## [113]  34244.00  36347.43  39779.71  42018.71  39372.57  33444.00  29255.86
## [120]  31640.14  29671.14  31023.71  39723.43  39314.14  38239.86  34649.43
## [127]  36688.43  42867.57  42226.86  32155.14  33603.00  37254.43  33145.57
## [134]  31299.43  30252.00  26310.71  27929.86  27666.14  25017.57  27335.00
## [141]  25760.71  18436.86  21906.00  19418.14  22826.14  23444.29  25264.86
## [148]  25473.29  27366.86  28855.86  32326.86  27141.43  26297.71  23499.14
## [155]  30246.29  39931.86  38020.43  35004.00  40750.86  42363.29  46273.57
## [162]  41083.29  35711.29  41921.71  60583.29  63115.57  61300.14  57666.43
## [169]  55834.00  58927.71  57810.57  48987.14  52219.29  56503.57  56545.00
## [176]  64705.57  53833.29  50114.00  39592.43  29907.29  33923.29  45489.00
## [183]  44866.29  51680.57  58257.00  70600.57  76648.00  69430.14  69651.57
## [190]  77745.14  72795.86  67670.71  55357.86  48524.00  50154.43  45111.57
## [197]  36147.00  43501.57  41472.43  41058.00  41605.57  49382.86  59558.57
## [204]  59134.57  61109.00  63004.43  67344.29  78180.86  69117.86  55597.57
## [211]  49426.14  39119.43  35636.86  39201.14  27777.00  47207.00  55587.29
## [218]  56619.71  82679.86  91259.57  93552.71 102242.71  91884.00  85013.86
## [225]  84535.29  80700.43  79740.57  85163.14  86724.86  80355.00  74875.14
## [232]  81347.00  66062.43  56946.43  47732.14  38129.71  42928.29  45392.57
## [239]  37895.43  30660.29  42430.86  35845.14  40350.43  31494.71  30013.29
## [246]  34197.57  37430.14  26932.43  33729.86  38081.43  44028.00  47139.71
## [253]  46558.86  58350.57  78380.00  78168.29  70510.86  72207.14  67881.00
## [260]  69536.43  62390.71  50113.14  45565.57  45805.29  41348.57  51426.86
## [267]  47160.57  51907.43  49751.43  54407.43  54746.29  61634.57  58926.43
## [274]  69999.29  63044.86  63285.29  61395.43  67969.43  60792.57  56859.14
## [281]  44899.43  43064.14  62790.29  69120.71  69589.43  66633.29  65588.57
## [288]  70168.57  74644.71  52891.00  41560.57  34704.86  46520.00  50231.00
## [295]  49216.71  76914.86  83720.71  84485.00  89765.00  87702.86  82013.86
## [302]  85982.43  57248.43  52968.43  52601.86  45493.29  42298.86  46423.71
## [309]  37898.00  36435.14  30209.57  34541.86  33604.71  37990.71  35683.43
## [316]  65201.86  62730.57  64589.14  73744.86  76477.71 105647.43 103790.29
## [323]  76122.29  74746.14  72865.71  63652.57  60358.29  25957.14  30178.43
## [330]  30681.57  33337.29  32582.71  39184.43  40415.71  34975.43  34076.14
## [337]  34221.14  28862.57  35729.86  36489.29  36785.14  37787.71  39832.14
## [344]  41917.86  41633.57  33557.00  22759.57  28877.86  27574.00  27104.71
## [351]  24376.14  29732.29  34030.00  39139.71  37066.57  38509.29  40957.29
## [358]  49423.00  50053.29  50284.14  53103.86  50223.00  49587.14  41167.71
## [365]  37958.71  33582.29  31039.43  26526.57  34869.43  37487.43  46514.43
## [372]  39613.43  38980.57  37306.14  36771.29  26317.00  31580.71  23626.57
## [379]  33035.71  44864.57  48946.14  46969.57  49249.57  56370.14  67228.71
## [386]  59457.29  53124.71  52814.14  61262.00  61861.14  71784.71  59313.29
## [393]  61107.00  60603.43  60012.57  58280.43  56862.71  41704.43  51533.00
## [400]  50388.71  49205.29  56533.29  47996.14  47207.57  45292.00  40343.43
## [407]  39004.86  36788.43  30027.57  39040.14  42390.14  36291.14  30668.29
## [414]  47693.00  52094.43  56592.57  47971.43  43762.43  42246.71  46352.43
## [421]  33094.86  32784.86  26212.43  32611.57  42144.86  50034.86  46332.00
## [428]  42976.29  39456.29  39328.29  35296.14  30875.43  27709.00  29513.29
## [435]  31630.43  29346.14  34916.86  42020.86  38303.00  37966.43  41408.14
## [442]  38988.14  43555.29  38114.00  27847.86  26517.00  39518.29  39153.71
## [449]  45623.14  40627.43  41027.71  42882.86  47139.43  35547.57  41099.00
## [456]  35859.57  44524.57  48554.29  51554.29  47810.29  50490.00  50720.71
## [463]  52720.71  52145.57  55515.57  52457.00  58239.57  50523.57  47788.57
## [470]  46170.00  42305.57  46605.57  55149.57  48769.57  50719.43  44753.71
## [477]  42898.00  46141.14  34022.57  26651.86  28791.86  31879.00  33584.71
## [484]  34690.43  27410.43  41755.00  49379.57  57198.86  51144.57  56677.43
## 
## $interrupt_var
##   [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
##  [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
##  [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [297] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [334] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [408] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [445] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [482] 2 2 2 2 2 2 2 2 2
## Levels: 0 1 2
## 
## $residuals
##            2            3            4            5            6            7 
##   2067.13043   4061.04510   -558.55793   2420.31199  -3010.14175    504.02178 
##            8            9           10           11           12           13 
##  -5677.49738  -1163.25465  -3939.02312   -365.06953  -4894.35253  -1532.29846 
##           14           15           16           17           18           19 
##   -823.12126    447.68657  -3189.08749   -307.79219  -2070.00428   6670.33130 
##           20           21           22           23           24           25 
##  -1531.87834  -1202.10992   1486.83222  -1193.74580    234.04288   1688.07743 
##           26           27           28           29           30           31 
##  -7126.86256    981.78170   8209.99126    359.88486    -72.77818  -2456.11899 
##           32           33           34           35           36           37 
##   1543.72946   4526.48061   1043.67002   2304.88509  -1968.17039   4531.96208 
##           38           39           40           41           42           43 
##   4258.95319  -2351.37908  -3028.60606  -1126.43318 -10746.62613   7375.53326 
##           44           45           46           47           48           49 
##   2570.37188   1356.25547   8083.96012    598.55372   6446.05069   6586.42829 
##           50           51           52           53           54           55 
##  -6051.41869  -4893.64917  -5105.22265  -7925.75602   6199.15434  -4068.36074 
##           56           57           58           59           60           61 
##  -4852.91385   3933.12564    922.31474     -9.89414    161.53868  -4981.13610 
##           62           63           64           65           66           67 
##  18182.14938   3534.96650  -3769.67100   5848.00891   7225.92622  14473.15619 
##           68           69           70           71           72           73 
##   1424.04825 -13462.47595  -1412.49372   4561.42859  -5011.66270  -4460.51733 
##           74           75           76           77           78           79 
## -10509.19581   2545.65442  -5351.99727   1151.23858  -6799.07612    663.83407 
##           80           81           82           83           84           85 
##  -2257.27996  -2589.03052  -3817.55703   -404.36616   2435.30757   3848.13228 
##           86           87           88           89           90           91 
##    519.15345   -452.09290    228.69737   4327.90787  -1177.34405   1147.85771 
##           92           93           94           95           96           97 
##  -2077.25405  -1038.23972    191.41271    284.99780  -7477.81151   2460.71008 
##           98           99          100          101          102          103 
##  -8562.91066  -2832.77504  -3920.27039  -1598.16396  -1125.50421   3310.30380 
##          104          105          106          107          108          109 
##  -2256.42215   2688.56474  -1098.24219   1032.61195   2632.79558  -3136.87756 
##          110          111          112          113          114          115 
##  -4681.14023   -773.59446   1977.50192  11741.64061  -1300.74183   2627.99703 
##          116          117          118          119          120          121 
##   4204.34264   3414.89812  -1206.80500  -4800.57755  -3757.70204   2321.95152 
##          122          123          124          125          126          127 
##  -1750.80021   1339.09957   8845.38537    759.98986     46.83348  -2595.70935 
##          128          129          130          131          132          133 
##   2611.27100   6991.32169    898.49973  -8607.88643   1726.65161   4100.57726 
##          134          135          136          137          138          139 
##  -3230.08301  -1450.81501   -869.31707  -3886.41419   1210.28447   -482.06505 
##          140          141          142          143          144          145 
##  -2897.95069   1756.41958  -1862.62710  -7797.42828   2133.84997  -3414.97224 
##          146          147          148          149          150          151 
##   2188.16475   -200.70864   1074.45034   -323.48490   1386.18157   1204.40691 
##          152          153          154          155          156          157 
##   3361.60198  -4886.43028  -1154.83120  -3208.95939   6007.47624   9739.77062 
##          158          159          160          161          162          163 
##  -3112.96056  -4442.85839   3965.51569    507.25976   2994.83453  -5645.65444 
##          164          165          166          167          168          169 
##  -6438.05541   4512.30604  17694.16514   3760.59157   -289.17512  -2321.06334 
##          170          171          172          173          174          175 
##   -947.31882   3763.22131    -83.63080  -7921.35904   3096.05173   4528.48707 
##          176          177          178          179          180          181 
##    789.71707   8913.73439  -9158.95357  -3285.18184 -10525.07704 -10926.61186 
##          182          183          184          185          186          187 
##   1634.98143   9657.21648  -1170.39533   6193.33639   6757.24505  13298.16755 
##          188          189          190          191          192          193 
##   8454.36378  -4099.38420   2490.65167  10388.84772  -1701.72344  -2459.91132 
##          194          195          196          197          198          199 
## -10250.64762  -6220.37288   1439.84427  -5041.60595  -9556.66055   5707.71485 
##          200          201          202          203          204          205 
##  -2810.66386  -1434.69761   -521.45909   6772.68220  10086.18273    683.73843 
##          206          207          208          209          210          211 
##   3032.27935   3185.58973   5853.03358  12860.37351  -5764.17177 -11287.80601 
##          212          213          214          215          216          217 
##  -5529.73963 -10391.15286  -4779.69340   1857.40595 -12711.65036  16798.33297 
##          218          219          220          221          222          223 
##   8034.74378   1672.90730  26822.09667  12407.89284   7130.80675  13797.47405 
##          224          225          226          227          228          229 
##  -4228.77872  -1959.00898   3624.22452    211.63030   2635.42273   8904.91494 
##          230          231          232          233          234          235 
##   5682.07512  -2065.74570  -1925.22024   9381.73651 -11613.21625  -7243.02065 
##          236          237          238          239          240          241 
##  -8413.89084  -9886.18243   3385.00012   1615.31217  -8056.16964  -8676.28012 
##          242          243          244          245          246          247 
##   9478.15050  -7493.21505   2822.91276 -10007.99734  -3675.67101   1815.73906 
##          248          249          250          251          252          253 
##   1356.34595 -11993.59685   4066.38964   2420.31490   4527.31885   2392.13264 
##          254          255          256          257          258          259 
##   -934.31588  11369.91215  20995.03426   3110.54500  -4360.07949   4092.66518 
##          260          261          262          263          264          265 
##  -1730.17918   3742.38022  -4863.98565 -10836.60434  -4551.17790   -298.95740 
##          266          267          268          269          270          271 
##  -4967.18129   9043.44370  -4115.32115   4395.85244  -1948.49161   4609.83439 
##          272          273          274          275          276          277 
##    840.51441   7429.81276  -1356.14320  12106.21793  -4618.24037   1758.36182 
##          278          279          280          281          282          283 
##   -343.63518   7897.86222  -5079.50103  -2680.49825 -11169.58941  -2452.33547 
##          284          285          286          287          288          289 
##  18893.15421   7818.40906   2701.53016   -668.17827    895.43186   6397.22553 
##          290          291          292          293          294          295 
##   6832.24913 -18870.94704 -11007.19361  -7865.61248   9998.60439   3284.62595 
##          296          297          298          299          300          301 
##  -1004.02516  27589.06301   9955.72838   4714.93105   9320.57085   2599.67043 
##          302          303          304          305          306          307 
##  -1269.81752   7718.38872 -24517.24237  -3444.07194    -34.22626  -6819.35689 
##          308          309          310          311          312          313 
##  -3741.60536   3201.82532  -8963.41727  -2903.69185  -7838.52525   1986.83395 
##          314          315          316          317          318          319 
##  -2772.85983   2440.01922  -3737.21170  27817.02838   -699.56082   3339.52596 
##          320          321          322          323          324          325 
##  10855.34750   5509.74760  32268.15149   4673.38986 -21355.97788   1680.47478 
##          326          327          328          329          330          331 
##   1014.27271  -6539.69196  -1704.84908 -33199.31040   1375.49510  -1845.97317 
##          332          333          334          335          336          337 
##    365.79783  -2732.01773   4535.48544    -58.18838  -6584.88728  -2683.98887 
##          338          339          340          341          342          343 
##  -1745.51271  -7232.02350   4363.34653   -936.50885  -1310.72625   -569.20155 
##          344          345          346          347          348          349 
##    590.61770    872.44996  -1252.14583  -9077.88047 -12749.02339   2896.27028 
##          350          351          352          353          354          355 
##  -3805.99782  -3124.83764  -5439.33930   2324.33250   1896.10529   3213.77243 
##          356          357          358          359          360          361 
##  -3367.87802    -95.94593   1079.08890   7384.83377    545.47735    220.20791 
##          362          363          364          365          366          367 
##   2836.22760  -2532.57751   -626.53787  -8484.92396  -4265.12159  -5810.11966 
##          368          369          370          371          372          373 
##  -4491.47686  -6760.66829   5564.06524    820.82494   7537.85764  -7328.02958 
##          374          375          376          377          378          379 
##  -1871.85529  -2987.88841  -2045.32949 -12027.68912   2460.26431 -10138.26657 
##          380          381          382          383          384          385 
##   6289.13826   9815.92808   3460.42040  -2117.48659   1906.52233   7015.35753 
##          386          387          388          389          390          391 
##  11591.16078  -5761.22457  -5236.75023     40.16225   8762.04913   1907.30593 
##          392          393          394          395          396          397 
##  11302.22938  -9925.16765   2872.59251    786.35441    639.81871   -570.98699 
##          398          399          400          401          402          403 
##   -460.36157 -14367.74063   8835.59940   -980.83243  -1154.61143   7217.57547 
##          404          405          406          407          408          409 
##  -7785.35819  -1041.26317  -2261.04613  -5519.43140  -2491.67810  -3527.03096 
##          410          411          412          413          414          415 
##  -8332.24353   6645.70562   2043.54931  -7011.29119  -7252.75394  14733.23464 
##          416          417          418          419          420          421 
##   4113.06918   4727.65358  -7862.38266  -4464.59961  -2266.54295   3176.54736 
##          422          423          424          425          426          427 
## -13703.66185  -2315.97034  -8614.87338   3583.38905   7470.45219   6948.84860 
##          428          429          430          431          432          433 
##  -3715.67467  -3804.20833  -4363.32588  -1385.48751  -5304.69079  -6167.68234 
##          434          435          436          437          438          439 
##  -5433.53589   -835.38217   -310.23358  -4462.56007   3123.67190   5312.40536 
##          440          441          442          443          444          445 
##  -4673.59832  -1729.75402   2008.93086  -3447.83287   3254.57387  -6216.48672 
##          446          447          448          449          450          451 
## -11681.56310  -3954.18716  10221.36765  -1614.76404   5176.34065  -5527.61214 
##          452          453          454          455          456          457 
##   -719.40565    782.54893   3402.25278 -11945.34950   3834.04349  -6303.63505 
##          458          459          460          461          462          463 
##   6984.32470   3368.55901   2812.97910  -3578.04224   2405.15468    271.44866 
##          464          465          466          467          468          469 
##   2067.88012   -271.94364   3605.52817  -2426.53057   6054.74214  -6763.45452 
##          470          471          472          473          474          475 
##  -2690.31562  -1895.68593  -4331.98346   3377.75818   8127.69425  -5791.02254 
##          476          477          478          479          480          481 
##   1788.16666  -5897.98545  -2489.90872   2390.60591 -12589.52164  -9267.53018 
##          482          483          484          485          486          487 
##   -624.05084    574.88346   -443.31326   -842.61969  -9098.23612  11669.77377 
##          488          489          490          491 
##   6637.54962   7729.36756  -5224.19021   5650.60810 
## 
## $fitted.values
##        2        3        4        5        6        7        8        9 
## 17202.16 20077.95 24374.70 24089.83 26466.86 23772.69 24496.21 19680.40 
##       10       11       12       13       14       15       16       17 
## 19414.31 16730.36 17515.64 14212.16 14263.84 14935.17 16648.80 14951.94 
##       18       19       20       21       22       23       24       25 
## 15997.00 15364.24 22517.88 21592.68 21067.31 22976.32 22295.53 22954.64 
##       26       27       28       29       30       31       32       33 
## 24819.15 18686.50 20430.01 28346.12 28404.35 28073.98 25679.56 27096.09 
##       34       35       36       37       38       39       40       41 
## 30977.76 31329.69 32753.03 30238.61 34184.05 37424.38 34450.89 31229.72 
##       42       43       44       45       46       47       48       49 
## 30065.91 20550.75 28145.06 30606.03 31706.18 38613.02 38102.52 42811.57 
##       50       51       52       53       54       55       56       57 
## 47090.42 39714.93 34228.79 29201.47 22276.99 28630.22 25176.49 21436.87 
##       58       59       60       61       62       63       64       65 
## 25889.54 27161.75 27461.75 27877.71 23707.14 40465.18 42327.67 37525.85 
##       66       67       68       69       70       71       72       73 
## 41775.07 46740.13 57515.52 55509.33 40604.21 38085.00 41133.23 35376.09 
##       74       75       76       77       78       79       80       81 
## 30782.62 21392.63 24626.28 20511.05 22618.08 17462.31 19497.99 18716.74 
##       82       83       84       85       86       87       88       89 
## 17734.70 15784.22 17074.84 20719.15 25181.28 26181.09 26206.30 26829.23 
##       90       91       92       93       94       95       96       97 
## 30995.77 29814.57 30823.97 28868.95 28060.73 28432.57 28843.24 22356.15 
##       98       99      100      101      102      103      104      105 
## 25401.48 18361.92 17206.56 15227.59 15530.36 16214.55 20732.14 19806.44 
##      106      107      108      109      110      111      112      113 
## 23352.81 23140.67 24833.63 27739.31 25212.28 21620.02 21898.21 24571.07 
##      114      115      116      117      118      119      120      121 
## 35544.74 33719.43 35575.37 38603.82 40579.38 38244.58 33013.56 29318.19 
##      122      123      124      125      126      127      128      129 
## 31421.94 29684.61 30878.04 38554.15 38193.02 37245.14 34077.16 35876.25 
##      130      131      132      133      134      135      136      137 
## 41328.36 40763.03 31876.35 33153.85 36375.65 32750.24 31121.32 30197.13 
##      138      139      140      141      142      143      144      145 
## 26719.57 28148.21 27915.52 25578.58 27623.34 26234.29 19772.15 22833.12 
##      146      147      148      149      150      151      152      153 
## 20637.98 23644.99 24190.41 25796.77 25980.68 27651.45 28965.26 32027.86 
##      154      155      156      157      158      159      160      161 
## 27452.55 26708.10 24238.81 30192.09 41133.39 39446.86 36785.34 41856.03 
##      162      163      164      165      166      167      168      169 
## 43278.74 46728.94 42149.34 37409.41 42889.12 59354.98 61589.32 59987.49 
##      170      171      172      173      174      175      176      177 
## 56781.32 55164.49 57894.20 56908.50 49123.23 51975.08 55755.28 55791.84 
##      178      179      180      181      182      183      184      185 
## 62992.24 53399.18 50117.51 40833.90 32288.30 35831.78 46036.68 45487.24 
##      186      187      188      189      190      191      192      193 
## 51499.75 57302.40 68193.64 73529.53 67160.92 67356.30 74497.58 70130.63 
##      194      195      196      197      198      199      200      201 
## 65608.50 54744.37 48714.58 50153.18 45703.66 37793.86 44283.09 42492.70 
##      202      203      204      205      206      207      208      209 
## 42127.03 42610.17 49472.39 58450.83 58076.72 59818.84 61491.25 65320.48 
##      210      211      212      213      214      215      216      217 
## 74882.03 66885.38 54955.88 49510.58 40416.55 37343.74 40488.65 30408.67 
##      218      219      220      221      222      223      224      225 
## 47552.54 54946.81 55857.76 78851.68 86421.91 88445.24 96112.78 86972.87 
##      226      227      228      229      230      231      232      233 
## 80911.06 80488.80 77105.15 76258.23 81042.78 82420.75 76800.36 71965.26 
##      234      235      236      237      238      239      240      241 
## 77675.64 64189.45 56146.03 48015.90 39543.29 43777.26 45951.60 39336.57 
##      242      243      244      245      246      247      248      249 
## 32952.71 43338.36 37527.52 41502.71 33688.96 32381.83 36073.80 38926.03 
##      250      251      252      253      254      255      256      257 
## 29663.47 35661.11 39500.68 44747.58 47493.17 46980.66 57384.97 75057.74 
##      258      259      260      261      262      263      264      265 
## 74870.94 68114.48 69611.18 65794.05 67254.70 60949.75 50116.75 46104.24 
##      266      267      268      269      270      271      272      273 
## 46315.75 42383.41 51275.89 47511.58 51699.92 49797.59 53905.77 54204.76 
##      274      275      276      277      278      279      280      281 
## 60282.57 57893.07 67663.10 61526.92 61739.06 60071.57 65872.07 59539.64 
##      282      283      284      285      286      287      288      289 
## 56069.02 45516.48 43897.13 61302.31 66887.90 67301.46 64693.14 63771.35 
##      290      291      292      293      294      295      296      297 
## 67812.47 71761.95 52567.77 42570.47 36521.40 46946.37 50220.74 49325.79 
##      298      299      300      301      302      303      304      305 
## 73764.99 79770.07 80444.43 85103.19 83283.67 78264.04 81765.67 56412.50 
##      306      307      308      309      310      311      312      313 
## 52636.08 52312.64 46040.46 43221.89 46861.42 39338.83 38048.10 32555.02 
##      314      315      316      317      318      319      320      321 
## 36377.57 35550.70 39420.64 37384.83 63430.13 61249.62 62889.51 70967.97 
##      322      323      324      325      326      327      328      329 
## 73379.28 99116.90 97478.26 73065.67 71851.44 70192.26 62063.13 59156.45 
##      330      331      332      333      334      335      336      337 
## 28802.93 32527.54 32971.49 35314.73 34648.94 40473.90 41560.32 36760.13 
##      338      339      340      341      342      343      344      345 
## 35966.66 36094.59 31366.51 37425.79 38095.87 38356.92 39241.53 41045.41 
##      346      347      348      349      350      351      352      353 
## 42885.72 42634.88 35508.59 25981.59 31380.00 30229.55 29815.48 27407.95 
##      354      355      356      357      358      359      360      361 
## 32133.89 35925.94 40434.45 38605.23 39878.20 42038.17 49507.81 50063.93 
##      362      363      364      365      366      367      368      369 
## 50267.63 52755.58 50213.68 49652.64 42223.84 39392.41 35530.91 33287.24 
##      370      371      372      373      374      375      376      377 
## 29305.36 36666.60 38976.57 46941.46 40852.43 40294.03 38816.62 38344.69 
##      378      379      380      381      382      383      384      385 
## 29120.45 33764.84 26746.58 35048.64 45485.72 49087.06 47343.05 49354.79 
##      386      387      388      389      390      391      392      393 
## 55637.55 65218.51 58361.46 52773.98 52499.95 59953.84 60482.48 69238.45 
##      394      395      396      397      398      399      400      401 
## 58234.41 59817.07 59372.75 58851.42 57323.08 56072.17 42697.40 51369.55 
##      402      403      404      405      406      407      408      409 
## 50359.90 49315.71 55781.50 48248.83 47553.05 45862.86 41496.54 40315.46 
##      410      411      412      413      414      415      416      417 
## 38359.81 32394.44 40346.59 43302.43 37921.04 32959.77 47981.36 51864.92 
##      418      419      420      421      422      423      424      425 
## 55833.81 48227.03 44513.26 43175.88 46798.52 35100.83 34827.30 29028.18 
##      426      427      428      429      430      431      432      433 
## 34674.40 43086.01 50047.67 46780.49 43819.61 40713.77 40600.83 37043.11 
##      434      435      436      437      438      439      440      441 
## 33142.54 30348.67 31940.66 33808.70 31793.19 36708.45 42976.60 39696.18 
##      442      443      444      445      446      447      448      449 
## 39399.21 42435.98 40300.71 44330.49 39529.42 30471.19 29296.92 40768.48 
##      450      451      452      453      454      455      456      457 
## 40446.80 46155.04 41747.12 42100.31 43737.18 47492.92 37264.96 42163.21 
##      458      459      460      461      462      463      464      465 
## 37540.25 45185.73 48741.31 51388.33 48084.85 50449.27 50652.83 52417.52 
##      466      467      468      469      470      471      472      473 
## 51910.04 54883.53 52184.83 57287.03 50478.89 48065.69 46637.55 43227.81 
##      474      475      476      477      478      479      480      481 
## 47021.88 54560.59 48931.26 50651.70 45387.91 43750.54 46612.09 35919.39 
##      482      483      484      485      486      487      488      489 
## 29415.91 31304.12 34028.03 35533.05 36508.66 30085.23 42742.02 49469.49 
##      490      491 
## 56368.76 51026.82 
## 
## $shapiro.test
## [1] 0
## 
## $levenes.test
## [1] 0
## 
## $autcorr
## [1] "No autocorrelation evidence"
## 
## $post_sums
## [1] "Post-Est Warning"
## 
## $adjr_sq
## [1] 0.8544
## 
## $fstat.bootstrap
## 
## ORDINARY NONPARAMETRIC BOOTSTRAP
## 
## 
## Call:
## boot::boot(data = x, statistic = f.stat, R = Reps, formula = depvar ~ 
##     ., parallel = parr)
## 
## 
## Bootstrap Statistics :
##        original     bias    std. error
## t1*    4.822765  0.5546905    2.898365
## t2* 1695.707166 28.1620358  246.336083
## WARNING: All values of t3* are NA
## 
## $itsa.plot
## 
## $booted.ints
##       Parameter    Lower CI Median F-value   Upper CI
## 1 interrupt_var    1.429618       4.958626   10.74304
## 2    lag_depvar 1346.460711    1709.549813 2151.03650

Ahora con las tendencias descompuestas

require(zoo)
require(scales)
Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha2=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
    dplyr::mutate(treat=ifelse(fecha2>"2019-W26",1,0)) %>% 
   dplyr::mutate(gasto= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
    gasto=="aspiradora"~"electrodomésticos/mantención casa",
                                            gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
                                            gasto=="Tina"~"electrodomésticos/mantención casa",
                                            gasto=="Nexium"~"Farmacia",
                                            gasto=="donaciones"~"donaciones/regalos",
                                            gasto=="Regalo chocolates"~"donaciones/regalos",
                                            gasto=="filtro piscina msp"~"electrodomésticos/mantención casa",
                                            gasto=="Chromecast"~"electrodomésticos/mantención casa",
                                            gasto=="Muebles ratan"~"electrodomésticos/mantención casa",
                                            gasto=="Vacuna Influenza"~"Farmacia",
                                            gasto=="Easy"~"electrodomésticos/mantención casa",
                                            gasto=="Sopapo"~"electrodomésticos/mantención casa",
                                            gasto=="filtro agua"~"electrodomésticos/mantención casa",
                                            gasto=="ropa tami"~"donaciones/regalos",
                                            gasto=="yaz"~"Farmacia",
                                            gasto=="Yaz"~"Farmacia",
                                            gasto=="Remedio"~"Farmacia",
                                            gasto=="Entel"~"VTR",
                                            gasto=="Kerosen"~"Gas/Bencina",
                                            gasto=="Parafina"~"Gas/Bencina",
                                            gasto=="Plata basurero"~"donaciones/regalos",
                                            gasto=="Matri Andrés Kogan"~"donaciones/regalos",
                                            gasto=="Wild Protein"~"Comida",
                                            gasto=="Granola Wild Foods"~"Comida",
                                            gasto=="uber"~"Transporte",
                                            gasto=="Uber Reñaca"~"Transporte",
                                            gasto=="filtro piscina mspa"~"electrodomésticos/mantención casa",
                                            gasto=="Limpieza Alfombra"~"electrodomésticos/mantención casa",
                                            gasto=="Aspiradora"~"electrodomésticos/mantención casa",
                                            gasto=="Limpieza alfombras"~"electrodomésticos/mantención casa",
                                            gasto=="Pila estufa"~"electrodomésticos/mantención casa",
                                            gasto=="Reloj"~"electrodomésticos/mantención casa",
                                            gasto=="Arreglo"~"electrodomésticos/mantención casa",
                                            gasto=="Pan Pepperino"~"Comida",
                                            gasto=="Cookidoo"~"Comida",
                                            gasto=="remedios"~"Farmacia",
                                            gasto=="Bendina Reñaca"~"Gas/Bencina",
                                            gasto=="Bencina Reñaca"~"Gas/Bencina",
                                            gasto=="Vacunas Influenza"~"Farmacia",
                                            gasto=="Remedios"~"Farmacia",
                                            gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
                                        T~gasto)) %>% 
    dplyr::group_by(gastador, fecha,gasto, .drop=F) %>%
    #dplyr::mutate(fecha_simp=week(parse_date(fecha))) %>% 
#    dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%#después de  diosi. Junio 24, 2019   
    dplyr::summarise(monto=sum(monto)) %>% 
    dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>% 
  ggplot2::ggplot(aes(x = fecha, y = monto, color=as.factor(gastador_nombre))) +
  #stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
  geom_line(size=1) +
  facet_grid(gasto~.)+
  #geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +

  geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
  labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") +
  ggtitle( "Figura 6. Gastos Semanales por Gastador e ítem (media)") +
  scale_y_continuous(labels = f <- function(x) paste0(x/1000)) + 
  scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
  scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
  guides(color = F)+
  sjPlot::theme_sjplot2() +
  theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
  theme(
    panel.border = element_blank(), 
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), 
    axis.line = element_line(colour = "black")
    )

autoplot(forecast::mstl(Gastos_casa$monto, lambda = "auto",iterate=5000000,start = 
lubridate::decimal_date(as.Date("2019-03-03"))))

 # scale_x_continuous(breaks = seq(0,400,by=30))
msts <- forecast::msts(Gastos_casa$monto,seasonal.periods = c(7,30.5,365.25),start = 
lubridate::decimal_date(as.Date("2019-03-03")))
#tbats <- forecast::tbats(msts,use.trend = FALSE)
#plot(tbats, main="Multiple Season Decomposition")
library(bsts)
library(CausalImpact)
ts_week_covid<-  
Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::group_by(fecha_week)%>%
    dplyr::summarise(gasto_total=sum(monto,na.rm=T)/1000,min_day=min(day))%>%
    dplyr::ungroup() %>% 
    dplyr::mutate(covid=dplyr::case_when(min_day>=as.Date("2020-03-17")~1,TRUE~0))%>%
    dplyr::mutate(covid=as.factor(covid))%>%
    data.frame()


ts_week_covid$gasto_total_na<-ts_week_covid$gasto_total
post_resp<-ts_week_covid$gasto_total[which(ts_week_covid$covid==1)]
ts_week_covid$gasto_total_na[which(ts_week_covid$covid==1)]<-NA
ts_week_covid$gasto_total[which(ts_week_covid$covid==0)]
##  [1]  98.357   4.780  56.784  50.506  64.483  67.248  49.299  35.786  58.503
## [10]  64.083  20.148  73.476 127.004  81.551  69.599 134.446  58.936  26.145
## [19] 129.927 104.989 130.860  81.893  95.697  64.579 303.471 151.106  49.275
## [28]  76.293  33.940  83.071 119.512  20.942  58.055  71.728  44.090  33.740
## [37]  59.264  77.410  60.831  63.376  48.754 235.284  29.604 115.143  72.419
## [46]   5.980  80.063 149.178  69.918 107.601  72.724  63.203  99.681 130.309
## [55] 195.898 112.066
# Model 1
ssd <- list()
# Local trend, weekly-seasonal #https://qastack.mx/stats/209426/predictions-from-bsts-model-in-r-are-failing-completely - PUSE UN GENERALIZED LOCAL TREND
ssd <- AddLocalLevel(ssd, ts_week_covid$gasto_total_na) #AddSemilocalLinearTrend #AddLocalLevel
# Add weekly seasonal
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na,nseasons=5, season.duration = 52) #weeks OJO, ESTOS NO SON WEEKS VERDADEROS. PORQUE TENGO MAS DE EUN AÑO
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na, nseasons = 12, season.duration =4) #years
# For example, to add a day-of-week component to data with daily granularity, use model.args = list(nseasons = 7, season.duration = 1). To add a day-of-week component to data with hourly granularity, set model.args = list(nseasons = 7, season.duration = 24).
model1d1 <- bsts(ts_week_covid$gasto_total_na, 
               state.specification = ssd, #A list with elements created by AddLocalLinearTrend, AddSeasonal, and similar functions for adding components of state. See the help page for state.specification.
               family ="student", #A Bayesian Analysis of Time-Series Event Count Data. POISSON NO SE PUEDE OCUPAR
               niter = 20000, 
               #burn = 200, #http://finzi.psych.upenn.edu/library/bsts/html/SuggestBurn.html Suggest the size of an MCMC burn in sample as a proportion of the total run.
               seed= 2125)
## =-=-=-=-= Iteration 0 Mon Sep 12 01:04:52 2022
##  =-=-=-=-=
## =-=-=-=-= Iteration 2000 Mon Sep 12 01:05:00 2022
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## =-=-=-=-= Iteration 4000 Mon Sep 12 01:05:09 2022
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## =-=-=-=-= Iteration 6000 Mon Sep 12 01:05:17 2022
##  =-=-=-=-=
## =-=-=-=-= Iteration 8000 Mon Sep 12 01:05:25 2022
##  =-=-=-=-=
## =-=-=-=-= Iteration 10000 Mon Sep 12 01:05:33 2022
##  =-=-=-=-=
## =-=-=-=-= Iteration 12000 Mon Sep 12 01:05:41 2022
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## =-=-=-=-= Iteration 14000 Mon Sep 12 01:05:50 2022
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## =-=-=-=-= Iteration 18000 Mon Sep 12 01:06:06 2022
##  =-=-=-=-=
#,
#               dynamic.regression=T)
#plot(model1d1, main = "Model 1")
#plot(model1d1, "components")

impact2d1 <- CausalImpact(bsts.model = model1d1,
                       post.period.response = post_resp)
plot(impact2d1)+
xlab("Date")+
  ylab("Monto Semanal (En miles)")

burn1d1 <- SuggestBurn(0.1, model1d1)
corpus <- Corpus(VectorSource(Gastos_casa$obs)) # formato de texto
d  <- tm_map(corpus, tolower)
d  <- tm_map(d, stripWhitespace)
d <- tm_map(d, removePunctuation)
d <- tm_map(d, removeNumbers)
d <- tm_map(d, removeWords, stopwords("spanish"))
d <- tm_map(d, removeWords, "menos")
tdm <- TermDocumentMatrix(d)
m <- as.matrix(tdm) #lo vuelve una matriz
v <- sort(rowSums(m),decreasing=TRUE) #lo ordena y suma
df <- data.frame(word = names(v),freq=v) # lo nombra y le da formato de data.frame
#findFreqTerms(tdm)
#require(devtools)
#install_github("lchiffon/wordcloud2")
#wordcloud2::wordcloud2(v, size=1.2)
wordcloud(words = df$word, freq = df$freq, 
          max.words=100, random.order=FALSE, rot.per=0.35, 
          colors=brewer.pal(8, "Dark2"), main="Figura 7. Nube de Palabras, Observaciones")

fit_month_gasto <- Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
  dplyr::mutate(gasto2= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
    gasto=="aspiradora"~"electrodomésticos/mantención casa",
                                            gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
                                            gasto=="Tina"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Nexium"~"Farmacia",
                                            gasto=="donaciones"~"donaciones/regalos",
                                            gasto=="Regalo chocolates"~"donaciones/regalos",
                                            gasto=="filtro piscina msp"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Chromecast"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Muebles ratan"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Vacuna Influenza"~"Farmacia",
                                            gasto=="Easy"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Sopapo"~"Electrodomésticos/ Mantención casa",
                                            gasto=="filtro agua"~"Electrodomésticos/ Mantención casa",
                                            gasto=="ropa tami"~"donaciones/regalos",
                                            gasto=="yaz"~"Farmacia",
                                            gasto=="Yaz"~"Farmacia",
                                            gasto=="Remedio"~"Farmacia",
                                            gasto=="Entel"~"VTR",
                                            gasto=="Kerosen"~"Gas/Bencina",
                                            gasto=="Parafina"~"Gas/Bencina",
                                            gasto=="Plata basurero"~"donaciones/regalos",
                                            gasto=="Matri Andrés Kogan"~"donaciones/regalos",
                                            gasto=="Wild Protein"~"Comida",
                                            gasto=="Granola Wild Foods"~"Comida",
                                            gasto=="uber"~"Otros",
                                            gasto=="Uber Reñaca"~"Otros",
                                            gasto=="filtro piscina mspa"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Limpieza Alfombra"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Aspiradora"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Limpieza alfombras"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Pila estufa"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Reloj"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Arreglo"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Pan Pepperino"~"Comida",
                                            gasto=="Cookidoo"~"Comida",
                                            gasto=="remedios"~"Farmacia",
                                            gasto=="Bendina Reñaca"~"Gas/Bencina",
                                            gasto=="Bencina Reñaca"~"Gas/Bencina",
                                            gasto=="Vacunas Influenza"~"Farmacia",
                                            gasto=="Remedios"~"Farmacia",
                                            gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
                                        T~gasto)) %>% 
  dplyr::mutate(fecha_month=factor(fecha_month, levels=format(seq(from = as.Date("2019-03-03"), to = as.Date(substr(Sys.time(),1,10)), by = "1 month"),"%Y-%m")))%>% 
  dplyr::mutate(gasto2=factor(gasto2, levels=c("Agua", "Comida", "Comunicaciones","Electricidad", "Enceres", "Farmacia", "Gas/Bencina", "Diosi", "donaciones/regalos", "Electrodomésticos/ Mantención casa", "VTR", "Netflix", "Otros")))%>% 
    dplyr::group_by(fecha_month, gasto2, .drop=F)%>%
    dplyr::summarise(gasto_total=sum(monto, na.rm = T)/1000)%>%
  data.frame() %>% na.omit()

fit_month_gasto_23<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2023",fecha_month)) %>% 
    #sacar el ultimo mes
    dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()

fit_month_gasto_22<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2022",fecha_month)) %>% 
    #sacar el ultimo mes
    dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()

fit_month_gasto_21<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2021|2022",fecha_month)) %>% 
    #sacar el ultimo mes
    dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()


fit_month_gasto_20<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("202",fecha_month)) %>% 
    #sacar el ultimo mes
    dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame() %>% ungroup()

fit_month_gasto_23 %>% 
dplyr::right_join(fit_month_gasto_22,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_21,by="gasto2") %>% 
dplyr::right_join(fit_month_gasto_20,by="gasto2") %>% 
  janitor::adorn_totals() %>% 
  #dplyr::select(-3)%>% 
  knitr::kable(format = "markdown", size=12, col.names= c("Item","2023","2022","2021","2020"))
Item 2023 2022 2021 2020
Agua NA 5.496500 5.70810 7.294219
Comida NA 285.084375 304.77170 337.832438
Comunicaciones NA 0.000000 0.00000 0.000000
Electricidad NA 48.842750 37.25090 31.008531
Enceres NA 13.849375 14.42045 23.642281
Farmacia NA 2.747500 9.49665 11.199188
Gas/Bencina NA 56.943750 30.92770 25.801687
Diosi NA 16.628375 38.26405 37.835531
donaciones/regalos NA 0.000000 8.60410 8.584969
Electrodomésticos/ Mantención casa NA 5.916000 36.32340 25.920875
VTR NA 27.240000 22.34815 21.135250
Netflix NA 7.607125 7.26010 7.630281
Otros NA 4.726625 1.89065 1.181656
Total 0 475.082375 517.26595 539.066906
## Joining, by = "word"


2. UF Proyectada

Saqué la UF proyectada

#options(max.print=5000)

uf18 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2018.htm")%>% rvest::html_nodes("table")
uf19 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2019.htm")%>% rvest::html_nodes("table")
uf20 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2020.htm")%>% rvest::html_nodes("table")
uf21 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2021.htm")%>% rvest::html_nodes("table")
uf22 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2022.htm")%>% rvest::html_nodes("table")

tryCatch(uf23 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2023.htm")%>% rvest::html_nodes("table"),
    error = function(c) {
      uf23b <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
      
    }
  )

tryCatch(uf23 <-uf23[[length(uf23)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1),
    error = function(c) {
      uf23 <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
    }
)

uf_serie<-
bind_rows(
cbind.data.frame(anio= 2018, uf18[[length(uf18)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2019, uf19[[length(uf19)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2020, uf20[[length(uf20)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2021, uf21[[length(uf21)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2022, uf22[[length(uf22)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2023, uf23)
)

uf_serie_corrected<-
uf_serie %>% 
dplyr::mutate(month=plyr::revalue(tolower(.[[3]]),c("ene" = 1, "feb"=2, "mar"=3, "abr"=4, "may"=5, "jun"=6, "jul"=7, "ago"=8, "sep"=9, "oct"=10, "nov"=11, "dic"=12))) %>% 
  dplyr::mutate(value=stringr::str_trim(value), value= sub("\\.","",value),value= as.numeric(sub("\\,",".",value))) %>% 
  dplyr::mutate(date=paste0(sprintf("%02d", .[[2]])," ",sprintf("%02d",as.numeric(month)),", ",.[[1]]), date3=lubridate::parse_date_time(date,c("%d %m, %Y"),exact=T),date2=date3) %>% 
   na.omit()#%>%  dplyr::filter(is.na(date3))
## Warning: 35 failed to parse.
#Day of the month as decimal number (1–31), with a leading space for a single-digit number.
#Abbreviated month name in the current locale on this platform. (Also matches full name on input: in some locales there are no abbreviations of names.)

warning(paste0("number of observations:",nrow(uf_serie_corrected),",  min uf: ",min(uf_serie_corrected$value),",  min date: ",min(uf_serie_corrected $date3 )))
## Warning: number of observations:1743, min uf: 26799.01, min date: 2018-01-01
# 
# uf_proyectado <- readxl::read_excel("uf_proyectado.xlsx") %>% dplyr::arrange(Período) %>% 
#   dplyr::mutate(Período= as.Date(lubridate::parse_date_time(Período, c("%Y-%m-%d"),exact=T)))

ts_uf_proy<-
ts(data = uf_serie_corrected$value, 
   start = as.numeric(as.Date("2018-01-01")), 
   end = as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])), frequency = 1,
   deltat = 1, ts.eps = getOption("ts.eps"))

fit_tbats <- forecast::tbats(ts_uf_proy)


fr_fit_tbats<-forecast::forecast(fit_tbats, h=298)

La proyección de la UF a 298 días más 2022-10-09 00:04:58 sería de: 36.024 pesos// Percentil 95% más alto proyectado: 39.527,33

Ahora con un modelo ARIMA automático


arima_optimal_uf = forecast::auto.arima(ts_uf_proy)

  autoplotly::autoplotly(forecast::forecast(arima_optimal_uf, h=298), ts.colour = "darkred",
           predict.colour = "blue", predict.linetype = "dashed")%>% 
  plotly::layout(showlegend = F, 
          yaxis = list(title = "Gastos"),
         xaxis = list(
    title="Fecha",
      ticktext = as.list(seq(from = as.Date("2018-01-01"), 
                                  to = as.Date("2018-01-01")+length(fit_tbats$fitted.values)+298, by = 90)), 
      tickvals = as.list(seq(from = as.numeric(as.Date("2018-01-01")), 
                             to = as.numeric(as.Date("2018-01-01"))+length(fit_tbats$fitted.values)+298, by = 90)),
      tickmode = "array",
    tickangle = 90
    ))
fr_fit_tbats_uf<-forecast::forecast(arima_optimal_uf, h=298)
dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats)),variable) %>% dplyr::summarise(max=max(value)) %>% 
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_uf)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>% 
  dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>% 
  dplyr::arrange(variable) %>% 
  knitr::kable(format="markdown", caption="Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales",
               col.names= c("Item","UF Proyectada (TBATS)","UF Proyectada (ARIMA)"))
## No id variables; using all as measure variables
## No id variables; using all as measure variables
Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales
Item UF Proyectada (TBATS) UF Proyectada (ARIMA)
Lo.95 34620.70 34573.97
Lo.80 34807.56 34841.43
Point.Forecast 36024.04 38454.70
Hi.80 37925.43 43096.21
Hi.95 38971.37 45553.27


3. Gastos proyectados

Lo haré en base a 2 cálculos: el gasto semanal y el gasto mensual en base a mis gastos desde marzo de 2019. La primera proyección la hice añadiendo el precio del arriendo mensual y partiendo en 2 (porque es con yo y Tami). No se incluye el último mes.

Gastos_casa_nvo <- readr::read_csv(as.character(path_sec),
                               col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador",
                                             "link"),skip=1) %>% 
              dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
              dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
              dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))
Gastos_casa_m <-
Gastos_casa_nvo %>% dplyr::group_by(fecha_month)%>%
              dplyr::summarise(gasto_total=(sum(monto)+500000)/1000,fecha=first(fecha))%>%
              data.frame()

uf_serie_corrected_m <-
uf_serie_corrected %>% dplyr::mutate(ano_m=paste0(anio,"-",sprintf("%02d",as.numeric(month)))) %>%  dplyr::group_by(ano_m)%>%
              dplyr::summarise(uf=(mean(value))/1000,fecha=first(date3))%>%
              data.frame() %>% 
  dplyr::filter(fecha>="2019-02-28")
#Error: Error in standardise_path(file) : object 'enlace_gastos' not found

ts_uf_serie_corrected_m<-
ts(data = uf_serie_corrected_m$uf[-length(uf_serie_corrected_m$uf)], 
   start = 1, 
   end = nrow(uf_serie_corrected_m), 
   frequency = 1,
   deltat = 1, ts.eps = getOption("ts.eps"))

ts_gastos_casa_m<-
ts(data = Gastos_casa_m$gasto_total[-length(Gastos_casa_m$gasto_total)], 
   start = 1, 
   end = nrow(Gastos_casa_m), 
   frequency = 1,
   deltat = 1, ts.eps = getOption("ts.eps"))

fit_tbats_m <- forecast::tbats(ts_gastos_casa_m)

seq_dates<-format(seq(as.Date("2019/03/01"), by = "month", length = dim(Gastos_casa_m)[1]+12), "%m\n'%y")

autplo2t<-
  autoplotly::autoplotly(forecast::forecast(fit_tbats_m, h=12), ts.colour = "darkred",
           predict.colour = "blue", predict.linetype = "dashed")%>% 
  plotly::layout(showlegend = F, 
          yaxis = list(title = "Gastos (en miles)"),
         xaxis = list(
    title="Fecha",
      ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]), 
      tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
      tickmode = "array"#"array"
    )) 

autplo2t

Ahora asumiendo un modelo ARIMA, e incluimos como regresor al precio de la UF.

paste0("Optimo pero sin regresor")
## [1] "Optimo pero sin regresor"
arima_optimal = forecast::auto.arima(ts_gastos_casa_m)
arima_optimal
## Series: ts_gastos_casa_m 
## ARIMA(1,0,0) with non-zero mean 
## 
## Coefficients:
##          ar1      mean
##       0.3573  986.4061
## s.e.  0.1481   38.4438
## 
## sigma^2 = 27945:  log likelihood = -280.18
## AIC=566.35   AICc=566.97   BIC=571.63
paste0("Optimo pero con regresor")
## [1] "Optimo pero con regresor"
arima_optimal2 = forecast::auto.arima(ts_gastos_casa_m, xreg=as.numeric(ts_uf_serie_corrected_m[1:(length(Gastos_casa_m$gasto_total))]))
arima_optimal2
## Series: ts_gastos_casa_m 
## Regression with ARIMA(1,0,0) errors 
## 
## Coefficients:
##          ar1     xreg
##       0.4100  33.1028
## s.e.  0.1482   1.4427
## 
## sigma^2 = 29234:  log likelihood = -281.17
## AIC=568.34   AICc=568.95   BIC=573.62
forecast_uf<-
cbind.data.frame(fecha=as.Date(seq(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])),(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)]))+299),by=1), origin = "1970-01-01"),forecast::forecast(fit_tbats, h=300)) %>% 
  dplyr::mutate(ano_m=stringr::str_extract(fecha,".{7}")) %>% 
  dplyr::group_by(ano_m)%>%
              dplyr::summarise(uf=(mean(`Hi 95`,na.rm=T))/1000,fecha=first(fecha))%>%
            data.frame()
autplo2t2<-
  autoplotly::autoplotly(forecast::forecast(arima_optimal2,xreg=c(forecast_uf$uf[1],forecast_uf$uf), h=12), ts.colour = "darkred",
           predict.colour = "blue", predict.linetype = "dashed")%>% 
  plotly::layout(showlegend = F, 
          yaxis = list(title = "Gastos (en miles)"),
         xaxis = list(
    title="Fecha",
      ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]), 
      tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
      tickmode = "array"#"array"
    )) 

autplo2t2
fr_fit_tbats_m<-forecast::forecast(fit_tbats_m, h=12)
fr_fit_tbats_m2<-forecast::forecast(arima_optimal, h=12)
fr_fit_tbats_m3<-forecast::forecast(arima_optimal2, h=12,xreg=c(forecast_uf$uf[1],forecast_uf$uf))

dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m3)),variable) %>% dplyr::summarise(max=max(value)), dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m2)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>% 
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>% 
  dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>% 
  dplyr::arrange(variable) %>% 
  knitr::kable(format="markdown", caption="Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales",
               col.names= c("Item","Modelo ARIMA con regresor (UF)","Modelo ARIMA sin regresor","Modelo TBATS")) 
## No id variables; using all as measure variables
## No id variables; using all as measure variables
## No id variables; using all as measure variables
Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales
Item Modelo ARIMA con regresor (UF) Modelo ARIMA sin regresor Modelo TBATS
Lo.95 922.8188 635.6049 664.3815
Lo.80 1049.9955 757.0292 743.5117
Point.Forecast 1290.2380 986.4050 919.6053
Hi.80 1530.4805 1215.7809 1216.7059
Hi.95 1657.6572 1337.2051 1411.0561


4. Gastos mensuales (resumen manual)

path_sec2<- paste0("https://docs.google.com/spreadsheets/d/",Sys.getenv("SUPERSECRET"),"/export?format=csv&id=",Sys.getenv("SUPERSECRET"),"&gid=847461368")

Gastos_casa_mensual_2022 <- readr::read_csv(as.character(path_sec2),
                #col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador","link"),
                skip=0)
## Rows: 42 Columns: 4
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): mes_ano
## dbl (3): n, Andrés, Tami
## 
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.
head(Gastos_casa_mensual_2022,5) %>% 
  knitr::kable("markdown",caption="Resumen mensual, primeras 5 observaciones")
Resumen mensual, primeras 5 observaciones
n mes_ano Andrés Tami
1 marzo_2019 68268 175533
2 abril_2019 55031 152640
3 mayo_2019 192219 152985
4 junio_2019 84961 291067
5 julio_2019 205893 241389


(
Gastos_casa_mensual_2022 %>% 
    reshape2::melt(id.var=c("n","mes_ano")) %>%
  dplyr::mutate(gastador=as.factor(variable)) %>% 
  dplyr::select(-variable) %>% 
 ggplot2::ggplot(aes(x = n, y = value, color=gastador)) +
  scale_color_manual(name="Gastador", values=c("red", "blue"))+
  geom_line(size=1) +
  #geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
  labs(y="Gastos (en miles)",x="Meses", subtitle="Azul= Tami; Rojo= Andrés") +
  ggtitle( "Gastos Mensuales (total manual)") +
  scale_y_continuous(labels = f <- function(x) paste0(x/1000)) + 
#  scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
#  scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
 # guides(color = F)+
  sjPlot::theme_sjplot2() +
  theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
  theme(
    panel.border = element_blank(), 
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), 
    axis.line = element_line(colour = "black")
    )
) %>% ggplotly()


Session Info

Sys.getenv("R_LIBS_USER")
## [1] "D:\\a\\_temp\\Library"
sessionInfo()
## R version 4.1.2 (2021-11-01)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows Server x64 (build 20348)
## 
## Matrix products: default
## 
## locale:
## [1] LC_COLLATE=Spanish_Chile.1252  LC_CTYPE=Spanish_Chile.1252   
## [3] LC_MONETARY=Spanish_Chile.1252 LC_NUMERIC=C                  
## [5] LC_TIME=Spanish_Chile.1252    
## 
## attached base packages:
## [1] grid      stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] CausalImpact_1.2.7  bsts_0.9.8          BoomSpikeSlab_1.2.5
##  [4] Boom_0.9.10         MASS_7.3-54         scales_1.2.1       
##  [7] ggiraph_0.8.3       tidytext_0.3.4      DT_0.24            
## [10] autoplotly_0.1.4    rvest_1.0.3         plotly_4.10.0      
## [13] xts_0.12.1          forecast_8.17.0     wordcloud_2.6      
## [16] RColorBrewer_1.1-3  SnowballC_0.7.0     tm_0.7-8           
## [19] NLP_0.2-1           tsibble_1.1.2       forcats_0.5.2      
## [22] dplyr_1.0.10        purrr_0.3.4         tidyr_1.2.1        
## [25] tibble_3.1.8        ggplot2_3.3.6       tidyverse_1.3.2    
## [28] sjPlot_2.8.11       lattice_0.20-45     gridExtra_2.3      
## [31] plotrix_3.8-2       sparklyr_1.7.8      httr_1.4.4         
## [34] readxl_1.4.1        zoo_1.8-10          stringr_1.4.1      
## [37] stringi_1.7.8       DataExplorer_0.8.2  data.table_1.14.2  
## [40] reshape2_1.4.4      fUnitRoots_4021.80  plyr_1.8.7         
## [43] readr_2.1.2        
## 
## loaded via a namespace (and not attached):
##   [1] utf8_1.2.2          tidyselect_1.1.2    lme4_1.1-30        
##   [4] htmlwidgets_1.5.4   munsell_0.5.0       codetools_0.2-18   
##   [7] effectsize_0.7.0.5  its.analysis_1.6.0  withr_2.5.0        
##  [10] colorspace_2.0-3    ggfortify_0.4.14    highr_0.9          
##  [13] knitr_1.40          uuid_1.1-0          rstudioapi_0.14    
##  [16] TTR_0.24.3          labeling_0.4.2      emmeans_1.8.1-1    
##  [19] slam_0.1-50         bit64_4.0.5         farver_2.1.1       
##  [22] datawizard_0.5.1    fBasics_4021.92     rprojroot_2.0.3    
##  [25] vctrs_0.4.1         generics_0.1.3      xfun_0.32          
##  [28] R6_2.5.1            bitops_1.0-7        cachem_1.0.6       
##  [31] assertthat_0.2.1    networkD3_0.4       vroom_1.5.7        
##  [34] nnet_7.3-16         googlesheets4_1.0.1 gtable_0.3.1       
##  [37] spatial_7.3-14      timeDate_4021.104   rlang_1.0.5        
##  [40] forge_0.2.0         systemfonts_1.0.4   splines_4.1.2      
##  [43] lazyeval_0.2.2      gargle_1.2.1        selectr_0.4-2      
##  [46] broom_1.0.1         yaml_2.3.5          abind_1.4-5        
##  [49] modelr_0.1.9        crosstalk_1.2.0     backports_1.4.1    
##  [52] quantmod_0.4.20     tokenizers_0.2.1    tools_4.1.2        
##  [55] ellipsis_0.3.2      gplots_3.1.3        jquerylib_0.1.4    
##  [58] Rcpp_1.0.9          base64enc_0.1-3     fracdiff_1.5-1     
##  [61] haven_2.5.1         fs_1.5.2            magrittr_2.0.3     
##  [64] timeSeries_4021.104 lmtest_0.9-40       reprex_2.0.2       
##  [67] googledrive_2.0.0   mvtnorm_1.1-3       sjmisc_2.8.9       
##  [70] hms_1.1.2           evaluate_0.16       xtable_1.8-4       
##  [73] sjstats_0.18.1      ggeffects_1.1.3     compiler_4.1.2     
##  [76] KernSmooth_2.23-20  crayon_1.5.1        minqa_1.2.4        
##  [79] htmltools_0.5.3     tzdb_0.3.0          lubridate_1.8.0    
##  [82] DBI_1.1.3           sjlabelled_1.2.0    dbplyr_2.2.1       
##  [85] boot_1.3-28         Matrix_1.5-0        car_3.1-0          
##  [88] cli_3.4.0           quadprog_1.5-8      parallel_4.1.2     
##  [91] insight_0.18.2      igraph_1.3.4        pkgconfig_2.0.3    
##  [94] xml2_1.3.3          bslib_0.4.0         estimability_1.4.1 
##  [97] anytime_0.3.9       snakecase_0.11.0    janeaustenr_1.0.0  
## [100] digest_0.6.29       parameters_0.18.2   janitor_2.1.0      
## [103] rmarkdown_2.16      cellranger_1.1.0    curl_4.3.2         
## [106] gtools_3.9.3        urca_1.3-3          nloptr_2.0.3       
## [109] lifecycle_1.0.2     nlme_3.1-153        jsonlite_1.8.0     
## [112] tseries_0.10-51     carData_3.0-5       viridisLite_0.4.1  
## [115] fansi_1.0.3         pillar_1.8.1        fastmap_1.1.0      
## [118] glue_1.6.2          bayestestR_0.12.1   bit_4.0.4          
## [121] sass_0.4.2          performance_0.9.2   r2d3_0.2.6         
## [124] caTools_1.18.2
#save.image("__analisis.RData")

sesion_info <- devtools::session_info()
dplyr::select(
  tibble::as_tibble(sesion_info$packages),
  c(package, loadedversion, source)
) %>% 
  DT::datatable(filter = 'top', colnames = c('Row number' =1,'Variable' = 2, 'Percentage'= 3),
              caption = htmltools::tags$caption(
        style = 'caption-side: top; text-align: left;',
        '', htmltools::em('Packages')),
      options=list(
initComplete = htmlwidgets::JS(
        "function(settings, json) {",
        "$(this.api().tables().body()).css({
            'font-family': 'Helvetica Neue',
            'font-size': '50%', 
            'code-inline-font-size': '15%', 
            'white-space': 'nowrap',
            'line-height': '0.75em',
            'min-height': '0.5em'
            });",#;
        "}")))